Python Pandas在同一个len（）的两列中有不同的value_counts（）

Question

I have a pandas data frame that contains two columns, with trace numbers [col_1] and ID numbers [col_2]. Trace numbers can be duplicates, as can ID numbers - however, each trace & ID should correspond only a specific fellow in the adjacent column.

Each of my two columns are the same length, but have different unique value counts, which should be the same, as shown below:

in[1]:  Trace | ID
        1     | 5054
        2     | 8291
        3     | 9323
        4     | 9323
        ...   |
        100   | 8928

in[2]:  print('unique traces: ', df['Trace'].value_counts())
        print('unique IDs: ', df['ID'].value_counts())

out[3]: unique traces: 100
        unique IDs: 99

In the code above, the same ID number (9232) is represented by two Trace numbers (3 & 4) - how can I isolate these incidences? Thanks for looking!

Answer 1

By using the duplicated() function ( docs ), you can do the following:

df[df['ID'].duplicated(keep=False)]

By setting keep to False , we get all the duplicates (instead of excluding the first or the last one).

Which returns:

Trace   ID
2   3   9323
3   4   9323

Answer 2

You can use groupby and filter :

df.groupby('ID').filter(lambda x: x.Trace.nunique() > 1)

Output:

  Trace      ID
2     3  9323.0
3     4  9323.0

Answer 3

#this should tell you the index of Non-unique Trace or IDs.

df.groupby('ID').filter(lambda x: len(x)>1)
Out[85]: 
   Trace    ID
2      3  9323
3      4  9323

df.groupby('Trace').filter(lambda x: len(x)>1)
Out[86]: 
Empty DataFrame
Columns: [Trace, ID]
Index: []